Researchers developed APT-RL, a framework enabling quadrupedal robots to learn agile, perceptive multi-skill locomotion in the wild by first learning reusable locomotion representations from trajectory-optimization data and then using these as priors for reinforcement learning on complex terrain. The trajectory optimization generated 180,000 trajectories (15.5 hours of motion) in 8 minutes, containing both state trajectories and control inputs.
APT-RL first learns reusable locomotion representations from trajectory-optimization data and then uses these representations as priors for reinforcement learning on complex terrain.
Trajectory optimization based on single rigid body dynamics generated 180,000 trajectories (15.5 hours of motion) in 8 minutes. The dataset contains both state trajectories and their corresponding control inputs.
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